This Research to Practice Full Paper presents the experiences and lessons learned from five programs that provide financial awards and a holistic student support structure to low-income, academically talented students in Science, Technology, Engineering, and Mathematics (STEM). This report synthesizes the experiences of a diverse set of institutions, both public and private, that vary in size and geographic location. We have experience supporting students from a range of disciplines with an emphasis on students studying Computer Science. The goals of this work are to (1) outline the decisions that must be considered when designing a financial award program; (2) describe the interventions we have implemented and underline the institutional contexts that have led to their success; (3) describe the unique challenges posed by the COVID pandemic; and (4) highlight key elements necessary for successful program implementation. We specifically discuss the challenges we have encountered when implementing existing best practices. We report observations and results, some of which buttress those reported in the literature. Our work is intended to serve as a guide for educators who wish to implement programs to support students from financially disadvantaged and/or historically marginalized groups. By sharing our experiences and pain points, we hope to make it easier for them to design and implement effective programs adapted to their institutional needs and contexts.

This Birds-of-a-Feather session is for anyone interested in the NSF Scholarships in STEM (S-STEM) program, including current and former Principal Investigators (PIs) and those planning to apply. The S-STEM program funds scholarships and activities to support low-income, academically talented students in STEM. Any institution of higher education may apply, and the program supports a variety of projects. Designing and implementing a successful S-STEM project is challenging. The goal of this session is to catalyze a community of practice for S-STEM PIs. It will provide an opportunity to discuss lessons learned and best practices for proposal writing, project implementation, and providing student support. Specific topics to be discussed include the following: (1) Understanding the solicitation requirements and common proposal mistakes; (2) Scholar recruitment and data-driven approaches for selection; (3) Cohort building including activities for students from different majors or class years and integration of new students into existing cohorts; and (4) Remediation strategies including proactive interventions and peer support. Session leaders will introduce each topic; participants will then join a breakout group discussion of one topic. Lastly, participants will be invited to join a Slack workspace dedicated to S-STEM best practices and lessons.

Managing energy in the home is key to creating a sustainable future for our society. More tools are increasingly available to measure home energy usage, however these tools provide little insight into questions such as why an appliance consumes more energy than normal or what kinds of behavioral changes might be most likely to reduce energy usage in the home. To answer these questions, a deeper understanding of the causal factors that influence energy usage is necessary. In this work, we conduct a broad study of factors that influence energy consumption of individual devices in the home. Our first contribution is collection of a context-rich data set from six homes across the United States. The second contribution of this work is a set of insights into key factors influencing energy usage derived by the novel application of a rule mining algorithm to identify significant associations between energy usage and four key features: hour of the day, day of the week, use of other appliances in the home, and user-supplied annotations of activities such as working or cooking. Our analysis confirms our hypothesis that, though most devices show a regular pattern of daily or weekly use, this is not true for all devices. Associations that relate use of two different devices in the same home are often stronger, and are observed for nearly 25% of device uses. Overall, we observe that the associations derived from the first five weeks of data in our data set are sufficient to explain nearly 70% of the device uses in the subsequent five weeks of data, and over 90% of the associations identified during the first five weeks recur in the latter portion of the data set. The associations identified by our approach may be used to to aid in end-user applications that heighten awareness and encourage energy savings, improve energy disaggregation algorithms, or even detect anomalous uses that may signal problems in aging-in-place homes.

The field of wireless sensor networks is growing rapidly and there is increasing interest in providing students with a foundation in the area. Curriculum in the field, however, is fairly limited and most courses reach only advanced graduate students. Undergraduate students typically lack the background, for example in distributed systems and networking, to digest the topics and assignments of a standard wireless sensor networks course. In this work, we present our approach to teaching wireless sensor networks to undergraduates and introductory graduate students. We discuss a unique, integrated approach to introducing relevant distributed systems and networking concepts in the context of wireless sensor networking applications. The course provides students who have never previously implemented a networked application with the necessary background to implement sensing applications on the Java SunSPOT sensor. It also provides a structured introduction to prerequisite concepts, such as distributed coordination algorithms, so that students can read and understand research papers. Our experience suggests that there is ample opportunity to expand curricula in sensor networking and reach a broader population of students.

The ease of deployment of battery-powered and mobile systems is pushing the network edge far from powered infrastructures. A primary challenge in building untethered systems is offering powerful aggregation points and gateways between heterogeneous end-points---a role traditionally played by powered servers. Microservers are battery-powered in-network nodes that play a number of roles: processing data fromclients, aggregating data, providing responses to queries, and actingas a network gateway. Providing QoS guarantees for theseservices can be extremely energy intensive. Since increasedenergy consumption translates to a shorter lifetime, there is a need for a new way to provide these QoS guarantees at minimal energy consumption.

Maintaining optimal consistency in a distributed system requires that nodes be always-on to synchronize information. Unfortunately, mobile devices such as laptops do not have adequate battery capacity for constant processing and communication. Even by powering off unnecessary components, such as the screen and disk, current laptops only have a lifetime of a few hours. Although PDAs and sensors are similarly limited in lifetime, a PDA's power requirement is an order-of-magnitude smaller than a laptop's, and a sensor's is an order-of-magnitude smaller than a PDA's. By combining these diverse platforms into a single integrated laptop, we can reduce the power cost of always-on operation. This paper presents the design, implementation, and evaluation of Turducken, a Hierarchical Power Management architecture for mobile systems. We focus on a particular instantiation of HPM, which provides high levels of consistency in a laptop by integrating two additional low power processors. We demonstrate that a Turducken system can provide battery lifetimes of up to ten times that of a standard laptop for always-on operation and three times for a system that periodically sleeps.

Peer-to-peer (P2P) content exchange has recently gained attention from both the research and industrial communities. The dynamic nature of peer networks and the resource constraints of peer hosts have introduced a number of unique technical challenges that must be addressed to make large-scale P2P content exchange applications viable. In this work, we present and evaluate Pixie, an architecture that integrates one-to-many distribution of content and peer networks. Pixie provides a valuable data location service as well as a number of scalability properties both in terms of data location and content distribution. Our results indicate that, using a one-to-many scheme, we can significantly reduce the resources consumed in searching for and distributing content across peer networks. These scalability properties will become increasingly important as peer content exchange is extended to support more advanced applications.

Javelin is a Java-based infrastructure for global computing. This paper presents Javelin++, an extension of Javelin, intended to support a much larger set of computational hosts. Contributions to scalability and fault tolerance are presented. This is the focus of the paper. Two scheduling schemes are presented: probabilistic work stealing and deterministic work stealing. The distributed deterministic work stealing is integrated with a distributed deterministic eager scheduler, which is one of the paper's primary original contributions. An additional fault tolerance mechanism is implemented for replacing hosts that have failed or retreated. A Javelin++ API is sketched, then illustrated on a raytracing application. Performance results for the two schedulers are reported, indicating that Javelin++, with its broker network, scales better than the original Javelin. Copyright © 2000 John Wiley & Sons, Ltd.